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Accurate Model Development and Control of Chemical Processes

Published on March 2012 by B. J. Parvat, comR. K. Munje
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International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 2
March 2012
Authors: B. J. Parvat, comR. K. Munje
7d853fa8-f7e3-4900-a43b-9a90fdd4bade

B. J. Parvat, comR. K. Munje . Accurate Model Development and Control of Chemical Processes. International Conference in Computational Intelligence. ICCIA, 2 (March 2012), 36-40.

@article{
author = { B. J. Parvat, comR. K. Munje },
title = { Accurate Model Development and Control of Chemical Processes },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 2 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 36-40 },
numpages = 5,
url = { /proceedings/iccia/number2/5103-1015/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A B. J. Parvat
%A comR. K. Munje
%T Accurate Model Development and Control of Chemical Processes
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 2
%P 36-40
%D 2012
%I International Journal of Computer Applications
Abstract

In this paper an accurate, simplified, reliable mathematical model is developed for flow loop system. The developed model is validated by comparing its output with real time system output. For finding the mathematical model, input and output data are generated by setting long experimentation under normal condition on real time system in open loop configuration. This data is recorded in the process history of Distributed Control System simultaneously. With the help of recorded data model is formulated using system identification approach. It is a single input single output process with manipulating variable as control valve and controlling variable as transmitter. Controller is then developed using Linear Quadratic Gaussian (LQG) controller. The simulation results are compared with the conventional PI controller and have shown that LQG controller is superior controller than PI controller.

References
  1. Soderstrom, T. Stoica, P. 1989. System Identification. Prentice Hall. London. United Kingdom.
  2. Ljung, L. 1987. System Identification: Theory for the Users. Prentice Hall International.
  3. Ogata, K. 2010. Modern Control Engineering. Pearson Education. Fifth Edition.
  4. Luyben, W. Process Modeling Simulation and Control for Chemical Engineers. McGraw-Hill.
  5. Bequette, B. Process Control and Modeling, Design and Simulation. Prentice Hall of India.
  6. Matlab-6.5 System identification toolbox manual.
  7. Matlab-6.5 Control system toolbox manual.
  8. Athans, M. The role and use of the stochastic Linear-Quadratic-Gaussian problem in control system design. IEEE Transaction on Automatic Control AC-16 (6): 529–552.
Index Terms

Computer Science
Information Sciences

Keywords

LQG Controller PI Controller Mathematical Model